Pneumonia is a leading cause of hospitalization and death. Antibiotics are the mainstay of treatment, but increasing antibiotic resistance among the bacteria that cause pneumonia threatens our ability to treat this disease. Patients who develop pneumonia after contact with the healthcare system are designated as having healthcare-associated pneumonia (HCAP), and are at increased risk for harboring bacteria that are resistant to the usual antibiotics. When such patients develop pneumonia, current guidelines recommend they be treated with 3 different antibiotics simultaneously to ensure adequate treatment for resistant organisms. However, such broad treatment can harm patients, either through direct toxicity (e.g. kidney damage) or through the development of superinfections with other bacteria (e.g. C. difficile infection, which can cause life-threatening diarrhea). Widespread use of unnecessary antibiotics can also increase the prevalence of resistant organisms, putting additional patients at risk. In caring for patients with HCAP, US physicians face several challenges. First, just because a patient was exposed to the healthcare system does not mean that the patient has a resistant infection. In fact, most patients with HCAP would be better off with standard therapy. However, physicians do not have a way of accurately determining an individual patient's risk for a resistant infection. Second, even if they knew the patient was at high risk for having a resistant infection, they do not know which antibiotic it will be resistant o until cultures are available, which usually takes several days. The aim of this proposal is to use data from a large national sample of patients to create tools that physicians can use to assess an individual patient's risk of having a resistant infection and to choose the appropriate antibiotic. An experienced team will develop and test these tools, and then incorporate them into a widely-used commercially available electronic health record (EHR) in the form of a smart order set that will make a personalized antibiotic recommendation. We will then assess the effects of the order set on physician behavior and patient outcomes in a randomized trial. Examining the effectiveness of an electronic decision aid embedded in an EHR will test whether a smart order set can safely reduce antibiotic overuse by incorporating patient-specific factors into this complex decision process. This proposal builds upon our inter-disciplinary team's strong foundation of creating risk assessment tools and incorporating them into the EHR. Knowledge to be gained will inform best practices for HCAP treatment for hundreds of thousands of patients each year.